Why This Job is Featured on The SaaS Jobs
Within the SaaS ecosystem, a Senior Data Engineer role like this stands out because it sits at the intersection of product analytics and platform reliability. Jellyfish’s focus on engineering signals implies a data platform that must ingest event-like operational data, normalize it into durable models, and serve it back to the application safely. The emphasis on orchestration, CI/CD for data, and caching-aware integration reflects patterns common in SaaS products where data is part of the customer experience, not just internal reporting.
For a SaaS career, this position builds durable leverage in modern data platform practices: treating pipelines as deployable software, enforcing data quality before production, and designing models that remain understandable as the business evolves. Exposure to infrastructure-as-code, warehouse governance, and incident response also maps closely to how subscription software companies keep analytics trustworthy while controlling operational risk.
This role is best suited to an engineer who enjoys owning end-to-end data delivery and reducing manual intervention through automation. It fits someone comfortable collaborating across data, backend, and product engineering, and who prefers pragmatic tradeoffs over academic purity. The on-call and observability elements signal a mindset oriented toward production stewardship and measurable reliability.
The section above is editorial commentary from The SaaS Jobs, provided to help SaaS professionals understand the role in a broader industry context.
Job Description
Jellyfish is the backbone for elite engineering organizations, and our data pipelines need to be as high-performing and reliable as the teams we serve. We are looking for a Senior Data Engineer to help us build, automate, and execute the next generation of our Jellyfish data platform. Working closely with our Lead Data Architect, you’ll be responsible for implementing core data models, building production-grade CI/CD for data pipelines, and transforming raw engineering signals into highly optimized analytical layers. If you view broken pipelines and manual data patches as a technical debt to be solved and want to write code that directly impacts how the world’s best engineering leaders measure their output, you’re the perfect fit.
What you’ll actually be doing:
Pipeline Execution & Modeling – You’ll maintain our end-to-end data pipelines, writing clean, modular Python and SQL. You will help translate the architectural blueprint into reality, structuring data across our Medallion layers (Bronze > Silver > Gold) for maximum performance and reliability.
Orchestration Modernization – You’ll take the lead on migrating, optimizing, and maintaining our workflow orchestration engines. You’ll eliminate pipeline bottlenecks, leverage modern fast-paths (like Pydantic v2 and async database clients), and ensure distributed tasks scale seamlessly without hitting API limits.
Data CI/CD & Infrastructure Automation – You’ll build the "paved road" for data deployments. You’ll use Terraform to provision data resources and write automated tests to validate schemas and data quality before code ever hits our isolated staging or production catalogs.
API & Caching Integration – You’ll collaborate with product developers to expose data safely. You’ll help design and optimize the application backend tiers, backend-for-frontend (BFF) layers, and Redis caching structures that protect our core data warehouse from frontend concurrency spikes.
On-Call & Observability Triage – You’ll participate in the data platform's incident response rotation. You won't just patch a failing pipeline; you’ll build deep observability, refine alerts to reduce noise, and write programmatic fixes to ensure the issue never happens again.
You’re a great fit if:
Data Engineering Fluency – You have solid, production-level experience with Python, advanced SQL, and data transformation frameworks (like dbt or PySpark). You are highly comfortable working with programmatic orchestrators (such as Prefect, Dagster, or Airflow).
Warehouse & Catalog Practitioner – You know your way around enterprise data platforms (e.g., Snowflake, Databricks, BigQuery). You understand how to safely navigate environment boundaries, manage access keys securely, and write performant queries that don't balloon the cloud bill.
Automation Mindset – You look at a repeated data backfill, a manual schema fix, or an untracked data quality bug and immediately think about how to script a permanent, automated solution.
Collaborative Builder – You love working in a team. You write readable code, value thorough documentation and clear data lineage, and enjoy collaborating with application engineers to solve complex data delivery problems.
Pragmatic Problem Solver – You know when to write a perfectly optimized distributed processing job and when a simple, well-indexed database table or cached view is the smartest move to keep the business moving.
Bonus Points:
You’ve survived (and thrived in) a rapidly scaling startup handling complex, multi-tenant B2B SaaS data.
You have strong opinions on data quality testing frameworks (like Great Expectations or Soda) and data-observability patterns.
You’ve worked extensively with cloud cost allocation or tracked token-level spend for LLM/AI model integrations.
A list of job experiences and qualification requirements is great, but humility, a performance-driven attitude, and a team-player approach are most important to us. We love to have fun and win in the process. We only hire people who have a passion for building great companies in an environment where a sense of humor is a must.
Occasional travel may be required.
Applicants must be authorized to work for any employer in the US. We are unable to sponsor or take over sponsorship of an employment visa at this time.
Let’s talk about us!
This is all about you, but you want to know a little about us. Jellyfish enables leaders to effectively build AI-integrated engineering teams, align engineering decisions with business initiatives and deliver the right software efficiently and on time. AI tools alone won’t transform your org—Jellyfish shows you what’s working, what’s not, and how to build high-performing teams that know how to use AI the right way.
